In many cases cancer can be cured when detected at an early, organ-confined stage. To facilitate early detection, there are considerable efforts do develop new biomarkers that improve current diagnosis and prognosis methods for cancer diseases. The identification and analysis of proteins associated with disease is a major challenge. Although several biomarkers for tumor diseases such as the prostate specific antigen (PSA), the carcinoembryonic antigen (CEA) or the alpha-fetoprotein (AFP) have been identified and introduced successfully into clinical practice, their sensitivity and specificity have been limited. A good example is prostate cancer, the most frequently diagnosed cancer and the second leading cause of cancer death in men in Western countries. The prostate marker PSA is quite sensitive, however, it does not correctly differentiate benign from malignant prostate disease, and can miss some significant prostate cancers. Therefore, further effort is warranted to search for additional biomarkers in order to improve cancer specificity. It is likely multiple biomarkers will be required to improve early detection, diagnosis and prognosis.
The classical technique for discovering disease-associated proteins is two-dimensional polyacrylamide gel electrophoresis (2D-PAGE) followed by the detection and identification of multiple protein species by matrix-assisted laser desorption ionization time of flight mass spectrometry1 (MALDI-TOF-MS). This technique is unchallenged in its ability to resolve thousands of proteins but it is laborious, requires large quantities of protein, lacks critical reproducibility, lacks standards and it is not easy to convert the results into a routinely used diagnostic test. Therefore more timesaving and robust techniques are needed. One technique is the ProteinChip approach produced by Ciphergen Biosystems Inc. (Fremont, Calif.). This method uses surface enhanced laser desorption/ionization (SELDI) TOF-MS to detect proteins affinity-bound to a protein chip array. There have been many examples of the use of SELDI for the determination of disease biomarkers, with the primary focus being diagnostics for all forms of cancer. Compared to conventional MS-applications, the SELDI-technology is much easier and timesaving regarding sample preparation and analysis. Other well established profiling techniques are based on different functionalized magnetic-particles and MALDI-TOF-MS and on the direct MALDI-TOF analysis of tissue sections. 1MALDI instrumentation and methods are described in references Fenn et al. Science, 246, 64, 1989, Karas et al. Anal. Chem., 60, 2299, 1995, Loo et al. Bioconjugate Chem., 6, 644, 1995, Chaurand et al. J. Am. Soc. Mass. Sectrom. 10, 91, 1999 and Loo et al. Anal. Chem. 65, 425, 1993.
In addition to those MS-based proteomics approaches we recently reported the development and optimization of material enhanced laser desorption ionization (MELDI) by introducing derivatized cellulose, silica beads and other extraction materials for the selective serum-protein profiling with a high-resolution MALDI-TOF MS instrument (Feuerstein et al., J. Proteome Res. 4:2320-2326). For derivatization, glycidyl methacrylate (GMA) was grafted onto 8 μm cellulose beads. In a second step iminodiacetic acid (IDA) was added to the cellulose beads through the epoxide of GMA. The functionalized beads were loaded with copper ions and mixed with serum samples in an Eppendorf tube. After binding of specific proteins (e.g. histidine, tryptophan, or cysteine containing proteins) from a sample, unbound proteins were washed and removed. Next, a small volume (e.g. 1 μl) of the protein-cellulose slurry was directly applied onto a MALDI-target, mixed with sinapinic acid (SA) and directly analyzed with MALDI TOF MS. All of these steps were performed manually.
Here, we describe the automation of the above-described MELDI approach including sample preparation and spotting using small extraction columns and a modified automated liquid handling system such as the MEA Personal Purification System™, commercially available from Phynexus, Inc. (San Jose, Calif.). Using this system, the automated method minimizes the analytical variance introduced by the human handling of samples and increases the robustness of the method.